Tianyong Hao
Biographic Data
| ID | 7950976 |
|---|---|
| NAME | Tianyong Hao |
| GIVEN NAMES | Tianyong |
| FAMILY NAME | Hao |
| SIGNATURE | HAO T |
| AFFILIATIONS | South China Normal University |
| ORCID | 0000-0002-9792-3949 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2021 |
| H-INDEX | 0 |
Detecting Symptom Errors in Neural Machine Translation of Patient Health Information on Depressive Disorders
Background: Due to its convenience, wide availability, low usage cost, neural machine translation (NMT) has increasing applications in diverse clinical settings and web-based self-diagnosis of diseases. Given the developing nature of NMT tools, this can pose safety risks to multicultural communities with limited bilingual skills, low education, and low health literacy. Research is needed to scrutinise the reliability, credibility, usability of au…
Predicting Risks of Machine Translations of Public Health Resources by Developing Interpretable Machine Learning Classifiers
We aimed to develop machine learning classifiers as a risk-prevention mechanism to help medical professionals with little or no knowledge of the patient's languages in order to predict the likelihood of clinically significant mistakes or incomprehensible MT outputs based on the features of English source information as input to the MT systems. A MNB classifier was developed to provide intuitive probabilistic predictions of erroneous health transl…
No prominent works on this page.
Detecting Symptom Errors in Neural Machine Translation of Patient Health Information on Depressive Disorders
Background: Due to its convenience, wide availability, low usage cost, neural machine translation (NMT) has increasing applications in diverse clinical settings and web-based self-diagnosis of diseases. Given the developing nature of NMT tools, this can pose safety risks to multicultural communities with limited bilingual skills, low education, and low health literacy. Research is needed to scrutinise the reliability, credibility, usability of au…
Predicting Risks of Machine Translations of Public Health Resources by Developing Interpretable Machine Learning Classifiers
We aimed to develop machine learning classifiers as a risk-prevention mechanism to help medical professionals with little or no knowledge of the patient's languages in order to predict the likelihood of clinically significant mistakes or incomprehensible MT outputs based on the features of English source information as input to the MT systems. A MNB classifier was developed to provide intuitive probabilistic predictions of erroneous health transl…
Artificial Intelligence (2 works) · Computer Science (2 works) · Machine learning (2 works) · Naive Bayes classifier (2 works) · Support vector machine (2 works) · Bayesian probability (1 work) · Biomedical Text Mining and Ontologies (1 work) · Classifier (UML (1 work) · Clinical Psychology (1 work) · Clinical Psychology (1 work)